code: def nanmean(
      a: paddle.Tensor,
      /,
      *,
      axis: Optional[Union[int, Tuple[int]]] = None,
      keepdims: Optional[bool] = False,
      dtype: Optional[paddle.dtype] = None,
      out: Optional[paddle.Tensor] = None,
  ) -> paddle.Tensor:
      ret_dtype = dtype if dtype is not None else a.dtype
      a = a.cast(ret_dtype)
      if paddle.is_complex(a):
          ret = paddle.complex(
              paddle.nanmean(a.real(), axis=axis, keepdim=keepdims),
              paddle.nanmean(a.imag(), axis=axis, keepdim=keepdims),
          )
      else:
          ret = paddle.nanmean(a, axis=axis, keepdim=keepdims)
  
      # The following code is to simulate other frameworks
      # output shapes behavior since min output dim is 1 in paddle
      if isinstance(axis, Sequence):
          if len(axis) == a.ndim:
              axis = None
      if (a.ndim == 1 or axis is None) and not keepdims:
          ret = ret.squeeze()
      return ret.astype(ret_dtype)
  
imports: from typing import Optional, Union, Tuple, Sequence, Any
  import paddle
  import ivy.functional.backends.paddle
  import ivy
  from copy import deepcopy
  from ivy.func_wrapper import with_unsupported_device_and_dtypes, with_supported_dtypes
  from .None import backend_version
label_desc: Use image node_id 2 for calling a global function with example usage: nanmean() and returns: ret